Load scripts: loads libraries and useful scripts used in the analyses; all .R files contained in scripts at the root of the factory are automatically loaded
Load data: imports datasets, and may contain some ad hoc changes to the data such as specific data cleaning (not used in other reports), new variables used in the analyses, etc.
library(reportfactory)
library(here)
library(rio)
library(tidyverse)
library(incidence)
library(distcrete)
library(epitrix)
library(earlyR)
library(projections)
library(linelist)
library(remotes)
library(janitor)
library(kableExtra)
library(DT)
library(cyphr)
library(chngpt)
library(lubridate)
library(ggpubr)
library(ggnewscale)These scripts will load:
.R files inside /scripts/.R files inside /src/These scripts also contain routines to access the latest clean encrypted data (see next section).
We import the latest NHS pathways data:
x <- import_pathways() %>%
as_tibble()
x
## [90m# A tibble: 161,896 x 11[39m
## site_type date sex age ccg_code ccg_name count postcode nhs_region
## [3m[90m<chr>[39m[23m [3m[90m<date>[39m[23m [3m[90m<chr>[39m[23m [3m[90m<chr>[39m[23m [3m[90m<chr>[39m[23m [3m[90m<chr>[39m[23m [3m[90m<int>[39m[23m [3m[90m<chr>[39m[23m [3m[90m<chr>[39m[23m
## [90m 1[39m 111 2020-03-18 fema… 0-18 e380000… nhs_dar… 6 da121au South East
## [90m 2[39m 111 2020-03-18 fema… 0-18 e380000… nhs_don… 9 dn45hz North Eas…
## [90m 3[39m 111 2020-03-18 fema… 0-18 e380000… nhs_eas… 7 rh80bt South East
## [90m 4[39m 111 2020-03-18 fema… 0-18 e380000… nhs_eas… 9 bn72pb South East
## [90m 5[39m 111 2020-03-18 fema… 0-18 e380000… nhs_far… 8 po176ar South East
## [90m 6[39m 111 2020-03-18 fema… 0-18 e380000… nhs_glo… 19 gl34fe South West
## [90m 7[39m 111 2020-03-18 fema… 0-18 e380000… nhs_hal… 10 wa75td North West
## [90m 8[39m 111 2020-03-18 fema… 0-18 e380000… nhs_ham… 1 nw15jd London
## [90m 9[39m 111 2020-03-18 fema… 0-18 e380000… nhs_har… 13 hg58qb North Eas…
## [90m10[39m 111 2020-03-18 fema… 0-18 e380000… nhs_has… 6 tn402dz South East
## [90m# … with 161,886 more rows, and 2 more variables: day [3m[90m<int>[90m[23m, weekday [3m[90m<fct>[90m[23m[39mWe also import demographics data for NHS regions in England, used later in our analysis:
path <- here::here("data", "csv", "nhs_region_population_2018.csv")
nhs_region_pop <- rio::import(path) %>%
mutate(nhs_region = str_to_title(gsub("_"," ",nhs_region)))
nhs_region_pop$nhs_region <- gsub(" Of ", " of ", nhs_region_pop$nhs_region)
nhs_region_pop$nhs_region <- gsub(" And ", " and ", nhs_region_pop$nhs_region)
nhs_region_pop
## nhs_region variable value
## 1 North West 0-18 0.22538599
## 2 North East and Yorkshire 0-18 0.21876449
## 3 Midlands 0-18 0.22564656
## 4 East of England 0-18 0.22810783
## 5 London 0-18 0.23764782
## 6 South East 0-18 0.22458811
## 7 South West 0-18 0.20799797
## 8 North West 19-69 0.64274078
## 9 North East and Yorkshire 19-69 0.64437753
## 10 Midlands 19-69 0.63876675
## 11 East of England 19-69 0.63034229
## 12 London 19-69 0.67820084
## 13 South East 19-69 0.63267336
## 14 South West 19-69 0.63176131
## 15 North West 70-120 0.13187323
## 16 North East and Yorkshire 70-120 0.13685797
## 17 Midlands 70-120 0.13558669
## 18 East of England 70-120 0.14154988
## 19 London 70-120 0.08415135
## 20 South East 70-120 0.14273853
## 21 South West 70-120 0.16024072Finally, we import publically available deaths per NHS region:
dth <- import_deaths() %>%
mutate(nhs_region = str_to_title(gsub("_"," ",nhs_region)))
#truncation to account for reporting delay
delay_max <- 21
dth$nhs_region <- gsub(" Of ", " of ", dth$nhs_region)
dth$nhs_region <- gsub(" And ", " and ", dth$nhs_region)
dth
## date_report nhs_region deaths
## 1 2020-03-01 East of England 0
## 2 2020-03-02 East of England 1
## 3 2020-03-03 East of England 0
## 4 2020-03-04 East of England 0
## 5 2020-03-05 East of England 0
## 6 2020-03-06 East of England 1
## 7 2020-03-07 East of England 0
## 8 2020-03-08 East of England 0
## 9 2020-03-09 East of England 1
## 10 2020-03-10 East of England 0
## 11 2020-03-11 East of England 0
## 12 2020-03-12 East of England 0
## 13 2020-03-13 East of England 1
## 14 2020-03-14 East of England 2
## 15 2020-03-15 East of England 2
## 16 2020-03-16 East of England 1
## 17 2020-03-17 East of England 1
## 18 2020-03-18 East of England 5
## 19 2020-03-19 East of England 4
## 20 2020-03-20 East of England 2
## 21 2020-03-21 East of England 11
## 22 2020-03-22 East of England 12
## 23 2020-03-23 East of England 11
## 24 2020-03-24 East of England 19
## 25 2020-03-25 East of England 26
## 26 2020-03-26 East of England 36
## 27 2020-03-27 East of England 38
## 28 2020-03-28 East of England 28
## 29 2020-03-29 East of England 43
## 30 2020-03-30 East of England 45
## 31 2020-03-31 East of England 70
## 32 2020-04-01 East of England 62
## 33 2020-04-02 East of England 64
## 34 2020-04-03 East of England 80
## 35 2020-04-04 East of England 71
## 36 2020-04-05 East of England 76
## 37 2020-04-06 East of England 71
## 38 2020-04-07 East of England 93
## 39 2020-04-08 East of England 111
## 40 2020-04-09 East of England 87
## 41 2020-04-10 East of England 74
## 42 2020-04-11 East of England 92
## 43 2020-04-12 East of England 101
## 44 2020-04-13 East of England 78
## 45 2020-04-14 East of England 61
## 46 2020-04-15 East of England 82
## 47 2020-04-16 East of England 74
## 48 2020-04-17 East of England 86
## 49 2020-04-18 East of England 64
## 50 2020-04-19 East of England 67
## 51 2020-04-20 East of England 67
## 52 2020-04-21 East of England 75
## 53 2020-04-22 East of England 67
## 54 2020-04-23 East of England 49
## 55 2020-04-24 East of England 66
## 56 2020-04-25 East of England 54
## 57 2020-04-26 East of England 48
## 58 2020-04-27 East of England 46
## 59 2020-04-28 East of England 58
## 60 2020-04-29 East of England 32
## 61 2020-04-30 East of England 45
## 62 2020-05-01 East of England 49
## 63 2020-05-02 East of England 29
## 64 2020-05-03 East of England 41
## 65 2020-05-04 East of England 19
## 66 2020-05-05 East of England 36
## 67 2020-05-06 East of England 31
## 68 2020-05-07 East of England 33
## 69 2020-05-08 East of England 33
## 70 2020-05-09 East of England 29
## 71 2020-05-10 East of England 22
## 72 2020-05-11 East of England 18
## 73 2020-05-12 East of England 21
## 74 2020-05-13 East of England 27
## 75 2020-05-14 East of England 26
## 76 2020-05-15 East of England 19
## 77 2020-05-16 East of England 26
## 78 2020-05-17 East of England 17
## 79 2020-05-18 East of England 25
## 80 2020-05-19 East of England 15
## 81 2020-05-20 East of England 26
## 82 2020-05-21 East of England 21
## 83 2020-05-22 East of England 13
## 84 2020-05-23 East of England 12
## 85 2020-05-24 East of England 17
## 86 2020-05-25 East of England 25
## 87 2020-05-26 East of England 14
## 88 2020-05-27 East of England 12
## 89 2020-05-28 East of England 17
## 90 2020-05-29 East of England 16
## 91 2020-05-30 East of England 9
## 92 2020-05-31 East of England 8
## 93 2020-06-01 East of England 17
## 94 2020-06-02 East of England 14
## 95 2020-06-03 East of England 10
## 96 2020-06-04 East of England 7
## 97 2020-06-05 East of England 12
## 98 2020-06-06 East of England 5
## 99 2020-06-07 East of England 9
## 100 2020-06-08 East of England 5
## 101 2020-06-09 East of England 6
## 102 2020-06-10 East of England 8
## 103 2020-06-11 East of England 0
## 104 2020-06-12 East of England 9
## 105 2020-06-13 East of England 5
## 106 2020-06-14 East of England 4
## 107 2020-06-15 East of England 7
## 108 2020-06-16 East of England 3
## 109 2020-06-17 East of England 7
## 110 2020-06-18 East of England 4
## 111 2020-06-19 East of England 7
## 112 2020-06-20 East of England 2
## 113 2020-06-21 East of England 3
## 114 2020-06-22 East of England 6
## 115 2020-06-23 East of England 4
## 116 2020-06-24 East of England 3
## 117 2020-06-25 East of England 0
## 118 2020-06-26 East of England 3
## 119 2020-06-27 East of England 1
## 120 2020-06-28 East of England 1
## 121 2020-03-01 London 0
## 122 2020-03-02 London 0
## 123 2020-03-03 London 0
## 124 2020-03-04 London 0
## 125 2020-03-05 London 0
## 126 2020-03-06 London 1
## 127 2020-03-07 London 0
## 128 2020-03-08 London 0
## 129 2020-03-09 London 1
## 130 2020-03-10 London 0
## 131 2020-03-11 London 6
## 132 2020-03-12 London 6
## 133 2020-03-13 London 10
## 134 2020-03-14 London 14
## 135 2020-03-15 London 10
## 136 2020-03-16 London 15
## 137 2020-03-17 London 23
## 138 2020-03-18 London 27
## 139 2020-03-19 London 25
## 140 2020-03-20 London 44
## 141 2020-03-21 London 49
## 142 2020-03-22 London 54
## 143 2020-03-23 London 63
## 144 2020-03-24 London 87
## 145 2020-03-25 London 113
## 146 2020-03-26 London 129
## 147 2020-03-27 London 130
## 148 2020-03-28 London 122
## 149 2020-03-29 London 146
## 150 2020-03-30 London 149
## 151 2020-03-31 London 181
## 152 2020-04-01 London 202
## 153 2020-04-02 London 191
## 154 2020-04-03 London 196
## 155 2020-04-04 London 230
## 156 2020-04-05 London 195
## 157 2020-04-06 London 197
## 158 2020-04-07 London 220
## 159 2020-04-08 London 238
## 160 2020-04-09 London 206
## 161 2020-04-10 London 170
## 162 2020-04-11 London 178
## 163 2020-04-12 London 158
## 164 2020-04-13 London 166
## 165 2020-04-14 London 144
## 166 2020-04-15 London 142
## 167 2020-04-16 London 139
## 168 2020-04-17 London 100
## 169 2020-04-18 London 101
## 170 2020-04-19 London 103
## 171 2020-04-20 London 95
## 172 2020-04-21 London 94
## 173 2020-04-22 London 109
## 174 2020-04-23 London 77
## 175 2020-04-24 London 71
## 176 2020-04-25 London 58
## 177 2020-04-26 London 53
## 178 2020-04-27 London 51
## 179 2020-04-28 London 43
## 180 2020-04-29 London 44
## 181 2020-04-30 London 40
## 182 2020-05-01 London 41
## 183 2020-05-02 London 41
## 184 2020-05-03 London 36
## 185 2020-05-04 London 30
## 186 2020-05-05 London 25
## 187 2020-05-06 London 37
## 188 2020-05-07 London 37
## 189 2020-05-08 London 30
## 190 2020-05-09 London 23
## 191 2020-05-10 London 26
## 192 2020-05-11 London 18
## 193 2020-05-12 London 18
## 194 2020-05-13 London 16
## 195 2020-05-14 London 20
## 196 2020-05-15 London 18
## 197 2020-05-16 London 14
## 198 2020-05-17 London 15
## 199 2020-05-18 London 9
## 200 2020-05-19 London 14
## 201 2020-05-20 London 19
## 202 2020-05-21 London 12
## 203 2020-05-22 London 10
## 204 2020-05-23 London 6
## 205 2020-05-24 London 7
## 206 2020-05-25 London 9
## 207 2020-05-26 London 12
## 208 2020-05-27 London 7
## 209 2020-05-28 London 8
## 210 2020-05-29 London 7
## 211 2020-05-30 London 12
## 212 2020-05-31 London 6
## 213 2020-06-01 London 10
## 214 2020-06-02 London 7
## 215 2020-06-03 London 6
## 216 2020-06-04 London 8
## 217 2020-06-05 London 4
## 218 2020-06-06 London 0
## 219 2020-06-07 London 4
## 220 2020-06-08 London 5
## 221 2020-06-09 London 4
## 222 2020-06-10 London 7
## 223 2020-06-11 London 5
## 224 2020-06-12 London 3
## 225 2020-06-13 London 3
## 226 2020-06-14 London 2
## 227 2020-06-15 London 1
## 228 2020-06-16 London 2
## 229 2020-06-17 London 1
## 230 2020-06-18 London 2
## 231 2020-06-19 London 3
## 232 2020-06-20 London 3
## 233 2020-06-21 London 4
## 234 2020-06-22 London 2
## 235 2020-06-23 London 0
## 236 2020-06-24 London 3
## 237 2020-06-25 London 3
## 238 2020-06-26 London 2
## 239 2020-06-27 London 0
## 240 2020-06-28 London 0
## 241 2020-03-01 Midlands 0
## 242 2020-03-02 Midlands 0
## 243 2020-03-03 Midlands 1
## 244 2020-03-04 Midlands 0
## 245 2020-03-05 Midlands 0
## 246 2020-03-06 Midlands 0
## 247 2020-03-07 Midlands 0
## 248 2020-03-08 Midlands 3
## 249 2020-03-09 Midlands 1
## 250 2020-03-10 Midlands 0
## 251 2020-03-11 Midlands 2
## 252 2020-03-12 Midlands 6
## 253 2020-03-13 Midlands 5
## 254 2020-03-14 Midlands 4
## 255 2020-03-15 Midlands 5
## 256 2020-03-16 Midlands 11
## 257 2020-03-17 Midlands 8
## 258 2020-03-18 Midlands 13
## 259 2020-03-19 Midlands 8
## 260 2020-03-20 Midlands 28
## 261 2020-03-21 Midlands 13
## 262 2020-03-22 Midlands 31
## 263 2020-03-23 Midlands 33
## 264 2020-03-24 Midlands 41
## 265 2020-03-25 Midlands 48
## 266 2020-03-26 Midlands 64
## 267 2020-03-27 Midlands 72
## 268 2020-03-28 Midlands 89
## 269 2020-03-29 Midlands 92
## 270 2020-03-30 Midlands 90
## 271 2020-03-31 Midlands 123
## 272 2020-04-01 Midlands 140
## 273 2020-04-02 Midlands 142
## 274 2020-04-03 Midlands 124
## 275 2020-04-04 Midlands 151
## 276 2020-04-05 Midlands 164
## 277 2020-04-06 Midlands 140
## 278 2020-04-07 Midlands 123
## 279 2020-04-08 Midlands 186
## 280 2020-04-09 Midlands 139
## 281 2020-04-10 Midlands 127
## 282 2020-04-11 Midlands 142
## 283 2020-04-12 Midlands 139
## 284 2020-04-13 Midlands 120
## 285 2020-04-14 Midlands 116
## 286 2020-04-15 Midlands 147
## 287 2020-04-16 Midlands 102
## 288 2020-04-17 Midlands 118
## 289 2020-04-18 Midlands 115
## 290 2020-04-19 Midlands 92
## 291 2020-04-20 Midlands 107
## 292 2020-04-21 Midlands 86
## 293 2020-04-22 Midlands 78
## 294 2020-04-23 Midlands 103
## 295 2020-04-24 Midlands 79
## 296 2020-04-25 Midlands 72
## 297 2020-04-26 Midlands 81
## 298 2020-04-27 Midlands 74
## 299 2020-04-28 Midlands 68
## 300 2020-04-29 Midlands 53
## 301 2020-04-30 Midlands 56
## 302 2020-05-01 Midlands 64
## 303 2020-05-02 Midlands 51
## 304 2020-05-03 Midlands 52
## 305 2020-05-04 Midlands 61
## 306 2020-05-05 Midlands 59
## 307 2020-05-06 Midlands 59
## 308 2020-05-07 Midlands 48
## 309 2020-05-08 Midlands 34
## 310 2020-05-09 Midlands 37
## 311 2020-05-10 Midlands 42
## 312 2020-05-11 Midlands 33
## 313 2020-05-12 Midlands 45
## 314 2020-05-13 Midlands 40
## 315 2020-05-14 Midlands 37
## 316 2020-05-15 Midlands 40
## 317 2020-05-16 Midlands 34
## 318 2020-05-17 Midlands 31
## 319 2020-05-18 Midlands 34
## 320 2020-05-19 Midlands 34
## 321 2020-05-20 Midlands 36
## 322 2020-05-21 Midlands 32
## 323 2020-05-22 Midlands 27
## 324 2020-05-23 Midlands 34
## 325 2020-05-24 Midlands 19
## 326 2020-05-25 Midlands 26
## 327 2020-05-26 Midlands 33
## 328 2020-05-27 Midlands 29
## 329 2020-05-28 Midlands 28
## 330 2020-05-29 Midlands 20
## 331 2020-05-30 Midlands 20
## 332 2020-05-31 Midlands 22
## 333 2020-06-01 Midlands 20
## 334 2020-06-02 Midlands 22
## 335 2020-06-03 Midlands 24
## 336 2020-06-04 Midlands 16
## 337 2020-06-05 Midlands 21
## 338 2020-06-06 Midlands 20
## 339 2020-06-07 Midlands 17
## 340 2020-06-08 Midlands 16
## 341 2020-06-09 Midlands 18
## 342 2020-06-10 Midlands 15
## 343 2020-06-11 Midlands 13
## 344 2020-06-12 Midlands 12
## 345 2020-06-13 Midlands 6
## 346 2020-06-14 Midlands 17
## 347 2020-06-15 Midlands 12
## 348 2020-06-16 Midlands 14
## 349 2020-06-17 Midlands 10
## 350 2020-06-18 Midlands 14
## 351 2020-06-19 Midlands 9
## 352 2020-06-20 Midlands 13
## 353 2020-06-21 Midlands 12
## 354 2020-06-22 Midlands 12
## 355 2020-06-23 Midlands 14
## 356 2020-06-24 Midlands 13
## 357 2020-06-25 Midlands 14
## 358 2020-06-26 Midlands 3
## 359 2020-06-27 Midlands 1
## 360 2020-06-28 Midlands 0
## 361 2020-03-01 North East and Yorkshire 0
## 362 2020-03-02 North East and Yorkshire 0
## 363 2020-03-03 North East and Yorkshire 0
## 364 2020-03-04 North East and Yorkshire 0
## 365 2020-03-05 North East and Yorkshire 0
## 366 2020-03-06 North East and Yorkshire 0
## 367 2020-03-07 North East and Yorkshire 0
## 368 2020-03-08 North East and Yorkshire 0
## 369 2020-03-09 North East and Yorkshire 0
## 370 2020-03-10 North East and Yorkshire 0
## 371 2020-03-11 North East and Yorkshire 0
## 372 2020-03-12 North East and Yorkshire 0
## 373 2020-03-13 North East and Yorkshire 0
## 374 2020-03-14 North East and Yorkshire 0
## 375 2020-03-15 North East and Yorkshire 2
## 376 2020-03-16 North East and Yorkshire 3
## 377 2020-03-17 North East and Yorkshire 1
## 378 2020-03-18 North East and Yorkshire 2
## 379 2020-03-19 North East and Yorkshire 6
## 380 2020-03-20 North East and Yorkshire 5
## 381 2020-03-21 North East and Yorkshire 6
## 382 2020-03-22 North East and Yorkshire 7
## 383 2020-03-23 North East and Yorkshire 9
## 384 2020-03-24 North East and Yorkshire 8
## 385 2020-03-25 North East and Yorkshire 18
## 386 2020-03-26 North East and Yorkshire 21
## 387 2020-03-27 North East and Yorkshire 28
## 388 2020-03-28 North East and Yorkshire 35
## 389 2020-03-29 North East and Yorkshire 38
## 390 2020-03-30 North East and Yorkshire 64
## 391 2020-03-31 North East and Yorkshire 60
## 392 2020-04-01 North East and Yorkshire 67
## 393 2020-04-02 North East and Yorkshire 74
## 394 2020-04-03 North East and Yorkshire 100
## 395 2020-04-04 North East and Yorkshire 105
## 396 2020-04-05 North East and Yorkshire 92
## 397 2020-04-06 North East and Yorkshire 96
## 398 2020-04-07 North East and Yorkshire 102
## 399 2020-04-08 North East and Yorkshire 107
## 400 2020-04-09 North East and Yorkshire 111
## 401 2020-04-10 North East and Yorkshire 117
## 402 2020-04-11 North East and Yorkshire 98
## 403 2020-04-12 North East and Yorkshire 84
## 404 2020-04-13 North East and Yorkshire 94
## 405 2020-04-14 North East and Yorkshire 107
## 406 2020-04-15 North East and Yorkshire 96
## 407 2020-04-16 North East and Yorkshire 103
## 408 2020-04-17 North East and Yorkshire 88
## 409 2020-04-18 North East and Yorkshire 95
## 410 2020-04-19 North East and Yorkshire 88
## 411 2020-04-20 North East and Yorkshire 100
## 412 2020-04-21 North East and Yorkshire 76
## 413 2020-04-22 North East and Yorkshire 84
## 414 2020-04-23 North East and Yorkshire 63
## 415 2020-04-24 North East and Yorkshire 72
## 416 2020-04-25 North East and Yorkshire 69
## 417 2020-04-26 North East and Yorkshire 65
## 418 2020-04-27 North East and Yorkshire 65
## 419 2020-04-28 North East and Yorkshire 57
## 420 2020-04-29 North East and Yorkshire 69
## 421 2020-04-30 North East and Yorkshire 57
## 422 2020-05-01 North East and Yorkshire 64
## 423 2020-05-02 North East and Yorkshire 48
## 424 2020-05-03 North East and Yorkshire 40
## 425 2020-05-04 North East and Yorkshire 49
## 426 2020-05-05 North East and Yorkshire 40
## 427 2020-05-06 North East and Yorkshire 51
## 428 2020-05-07 North East and Yorkshire 45
## 429 2020-05-08 North East and Yorkshire 42
## 430 2020-05-09 North East and Yorkshire 44
## 431 2020-05-10 North East and Yorkshire 40
## 432 2020-05-11 North East and Yorkshire 29
## 433 2020-05-12 North East and Yorkshire 27
## 434 2020-05-13 North East and Yorkshire 28
## 435 2020-05-14 North East and Yorkshire 31
## 436 2020-05-15 North East and Yorkshire 32
## 437 2020-05-16 North East and Yorkshire 35
## 438 2020-05-17 North East and Yorkshire 26
## 439 2020-05-18 North East and Yorkshire 30
## 440 2020-05-19 North East and Yorkshire 27
## 441 2020-05-20 North East and Yorkshire 22
## 442 2020-05-21 North East and Yorkshire 33
## 443 2020-05-22 North East and Yorkshire 22
## 444 2020-05-23 North East and Yorkshire 18
## 445 2020-05-24 North East and Yorkshire 26
## 446 2020-05-25 North East and Yorkshire 21
## 447 2020-05-26 North East and Yorkshire 21
## 448 2020-05-27 North East and Yorkshire 22
## 449 2020-05-28 North East and Yorkshire 21
## 450 2020-05-29 North East and Yorkshire 25
## 451 2020-05-30 North East and Yorkshire 20
## 452 2020-05-31 North East and Yorkshire 20
## 453 2020-06-01 North East and Yorkshire 17
## 454 2020-06-02 North East and Yorkshire 23
## 455 2020-06-03 North East and Yorkshire 23
## 456 2020-06-04 North East and Yorkshire 17
## 457 2020-06-05 North East and Yorkshire 18
## 458 2020-06-06 North East and Yorkshire 21
## 459 2020-06-07 North East and Yorkshire 14
## 460 2020-06-08 North East and Yorkshire 11
## 461 2020-06-09 North East and Yorkshire 12
## 462 2020-06-10 North East and Yorkshire 18
## 463 2020-06-11 North East and Yorkshire 7
## 464 2020-06-12 North East and Yorkshire 9
## 465 2020-06-13 North East and Yorkshire 10
## 466 2020-06-14 North East and Yorkshire 11
## 467 2020-06-15 North East and Yorkshire 9
## 468 2020-06-16 North East and Yorkshire 10
## 469 2020-06-17 North East and Yorkshire 9
## 470 2020-06-18 North East and Yorkshire 10
## 471 2020-06-19 North East and Yorkshire 6
## 472 2020-06-20 North East and Yorkshire 4
## 473 2020-06-21 North East and Yorkshire 4
## 474 2020-06-22 North East and Yorkshire 6
## 475 2020-06-23 North East and Yorkshire 7
## 476 2020-06-24 North East and Yorkshire 8
## 477 2020-06-25 North East and Yorkshire 3
## 478 2020-06-26 North East and Yorkshire 6
## 479 2020-06-27 North East and Yorkshire 2
## 480 2020-06-28 North East and Yorkshire 1
## 481 2020-03-01 North West 0
## 482 2020-03-02 North West 0
## 483 2020-03-03 North West 0
## 484 2020-03-04 North West 0
## 485 2020-03-05 North West 1
## 486 2020-03-06 North West 0
## 487 2020-03-07 North West 0
## 488 2020-03-08 North West 1
## 489 2020-03-09 North West 0
## 490 2020-03-10 North West 0
## 491 2020-03-11 North West 0
## 492 2020-03-12 North West 2
## 493 2020-03-13 North West 3
## 494 2020-03-14 North West 1
## 495 2020-03-15 North West 4
## 496 2020-03-16 North West 2
## 497 2020-03-17 North West 4
## 498 2020-03-18 North West 6
## 499 2020-03-19 North West 7
## 500 2020-03-20 North West 10
## 501 2020-03-21 North West 11
## 502 2020-03-22 North West 13
## 503 2020-03-23 North West 15
## 504 2020-03-24 North West 21
## 505 2020-03-25 North West 21
## 506 2020-03-26 North West 29
## 507 2020-03-27 North West 35
## 508 2020-03-28 North West 28
## 509 2020-03-29 North West 46
## 510 2020-03-30 North West 67
## 511 2020-03-31 North West 52
## 512 2020-04-01 North West 86
## 513 2020-04-02 North West 96
## 514 2020-04-03 North West 95
## 515 2020-04-04 North West 98
## 516 2020-04-05 North West 102
## 517 2020-04-06 North West 100
## 518 2020-04-07 North West 135
## 519 2020-04-08 North West 127
## 520 2020-04-09 North West 119
## 521 2020-04-10 North West 117
## 522 2020-04-11 North West 138
## 523 2020-04-12 North West 125
## 524 2020-04-13 North West 129
## 525 2020-04-14 North West 131
## 526 2020-04-15 North West 114
## 527 2020-04-16 North West 135
## 528 2020-04-17 North West 98
## 529 2020-04-18 North West 113
## 530 2020-04-19 North West 71
## 531 2020-04-20 North West 83
## 532 2020-04-21 North West 76
## 533 2020-04-22 North West 86
## 534 2020-04-23 North West 85
## 535 2020-04-24 North West 66
## 536 2020-04-25 North West 65
## 537 2020-04-26 North West 55
## 538 2020-04-27 North West 54
## 539 2020-04-28 North West 57
## 540 2020-04-29 North West 62
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## 597 2020-06-25 North West 11
## 598 2020-06-26 North West 2
## 599 2020-06-27 North West 1
## 600 2020-06-28 North West 1
## 601 2020-03-01 South East 0
## 602 2020-03-02 South East 0
## 603 2020-03-03 South East 1
## 604 2020-03-04 South East 0
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## 606 2020-03-06 South East 0
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## 611 2020-03-11 South East 1
## 612 2020-03-12 South East 0
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## 721 2020-03-01 South West 0
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## 732 2020-03-12 South West 0
## 733 2020-03-13 South West 0
## 734 2020-03-14 South West 1
## 735 2020-03-15 South West 0
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## 819 2020-06-07 South West 3
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## 821 2020-06-09 South West 0
## 822 2020-06-10 South West 1
## 823 2020-06-11 South West 2
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## 825 2020-06-13 South West 2
## 826 2020-06-14 South West 0
## 827 2020-06-15 South West 1
## 828 2020-06-16 South West 2
## 829 2020-06-17 South West 0
## 830 2020-06-18 South West 0
## 831 2020-06-19 South West 0
## 832 2020-06-20 South West 2
## 833 2020-06-21 South West 0
## 834 2020-06-22 South West 1
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## 837 2020-06-25 South West 0
## 838 2020-06-26 South West 3
## 839 2020-06-27 South West 0
## 840 2020-06-28 South West 0We extract the completion date from the NHS Pathways file timestamp:
The completion date of the NHS Pathways data is Monday 29 Jun 2020.
These are functions which will be used further in the analyses.
Function to estimate the generalised R-squared as the proportion of deviance explained by a given model:
## Function to calculate R2 for Poisson model
## not adjusted for model complexity but all models have the same DF here
Rsq <- function(x) {
1 - (x$deviance / x$null.deviance)
}Function to extract growth rates per region as well as halving times, and the associated 95% confidence intervals:
## function to extract the coefficients, find the level of the intercept,
## reconstruct the values of r, get confidence intervals
get_r <- function(model) {
## extract coefficients and conf int
out <- data.frame(r = coef(model)) %>%
rownames_to_column("var") %>%
cbind(confint(model)) %>%
filter(!grepl("day_of_week", var)) %>%
filter(grepl("day", var)) %>%
rename(lower_95 = "2.5 %",
upper_95 = "97.5 %") %>%
mutate(var = sub("day:", "", var))
## reconstruct values: intercept + region-coefficient
for (i in 2:nrow(out)) {
out[i, -1] <- out[1, -1] + out[i, -1]
}
## find the name of the intercept, restore regions names
out <- out %>%
mutate(nhs_region = model$xlevels$nhs_region) %>%
select(nhs_region, everything(), -var)
## find halving times
halving <- log(0.5) / out[,-1] %>%
rename(halving_t = r,
halving_t_lower_95 = lower_95,
halving_t_upper_95 = upper_95)
## set halving times with exclusion intervals to NA
no_halving <- out$lower_95 < 0 & out$upper_95 > 0
halving[no_halving, ] <- NA_real_
## return all data
cbind(out, halving)
}Functions used in the correlation analysis between NHS Pathways reports and deaths:
## Function to calculate Pearson's correlation between deaths and lagged
## reports. Note that `pearson` can be replaced with `spearman` for rank
## correlation.
getcor <- function(x, ndx) {
return(cor(x$deaths[ndx],
x$note_lag[ndx],
use = "complete.obs",
method = "pearson"))
}
## Catch if sample size throws an error
getcor2 <- possibly(getcor, otherwise = NA)
getboot <- function(x) {
result <- boot::boot.ci(boot::boot(x, getcor2, R = 1000),
type = "bca")
return(data.frame(n = sum(!is.na(x$note_lag) & !is.na(x$deaths)),
r = result$t0,
r_low = result$bca[4],
r_hi = result$bca[5]))
}Function to classify the day of the week into weekend, Monday, and the rest:
## Fn to add day of week
day_of_week <- function(df) {
df %>%
dplyr::mutate(day_of_week = lubridate::wday(date, label = TRUE)) %>%
dplyr::mutate(day_of_week = dplyr::case_when(
day_of_week %in% c("Sat", "Sun") ~ "weekend",
day_of_week %in% c("Mon") ~ "monday",
!(day_of_week %in% c("Sat", "Sun", "Mon")) ~ "rest_of_week"
) %>%
factor(levels = c("rest_of_week", "monday", "weekend")))
}Custom color palettes, color scales, and vectors of colors:
We look for temporal patterns in COVID-19 related 111/999 calls and 111 online reports. Analyses are broken down by NHS region. We also look for estimates of recent growth rate and associated doubling / halving time.
tab_date_region_all <- x %>%
filter(!is.na(nhs_region)) %>%
group_by(date, nhs_region) %>%
summarise(n = sum(count))
dth %>%
mutate(trusted = case_when(date_report < max(dth$date_report)-delay_max ~ "Y",
date_report >= max(dth$date_report)-delay_max ~ "N"),
value = "Deaths",
vline = max(dth$date_report)-delay_max-1,
lab = "Truncated for reporting delay",
lab_pos_x = vline + 10,
lab_pos_y = 150,
lab_col = "darkgrey") %>%
rename(date = date_report,
n = deaths) %>%
bind_rows(
mutate(tab_date_region_all, value = "Reports",
trusted = "Y",
vline = as.Date("2020-03-23"),
lab = "Start of UK lockdown",
lab_pos_x = vline - 8,
lab_pos_y = 30200,
lab_col = "black")
) %>%
mutate(value = factor(value, levels = c("Reports","Deaths"))) -> dths_reports
plot_dth_report <-
ggplot(dths_reports, aes(date, n, colour = nhs_region)) +
# Add main points and lines, coloured by region and fade out deaths for excluded period
geom_point(aes(alpha = trusted)) +
geom_line(alpha = 0.2) +
geom_smooth(method = "loess", span = .5, color = "black") +
scale_colour_manual("", values = pal) +
scale_alpha_manual(values = c(0.3,1)) +
guides(alpha = F) +
# Add vertical markers for important dates with labels - different for each facet
ggnewscale::new_scale_colour() +
geom_vline(aes(xintercept = vline, col = value), lty = "solid") +
geom_text(aes(x = lab_pos_x, y = lab_pos_y, label = lab, col = value), size = 3) +
scale_colour_manual("",values = c("black","darkgrey"), guide = F) +
# Facet by deaths and reports
facet_grid(rows = vars(value), scales = "free_y", switch = "y") +
# Other formatting
theme_bw() +
scale_weeks +
theme(legend.position = "bottom",strip.placement = "outside") +
rotate_x +
labs(x = NULL,
y = NULL)
plot_dth_reportWe plot the number of 111/999 calls and 111 online reports by age, and the proportion of 111/999 calls and 111 online reports by age. In the second graph, the vertical lines indicate the proportion of individuals residing in the corresponding NHS region who belong to the corresponding age group.
tab_date_region_age_all <- x %>%
filter(!is.na(nhs_region),
age != "missing") %>%
group_by(date, nhs_region, age) %>%
summarise(n = sum(count))
tab_date_region_age_all %>%
ggplot(aes(x = date, y = n, fill = age)) +
geom_col(position = "stack") +
scale_fill_manual(values = age.pal) +
theme_bw() +
scale_weeks +
theme(legend.position = "bottom",
axis.text.x = element_text(angle = 90, hjust = 1)) +
guides(fill = guide_legend(title = "Age", ncol = 3)) +
labs(x = NULL,
y = "Total daily reports by age") +
facet_wrap(~ nhs_region, ncol = 4)
tab_date_region_age_all <- tab_date_region_age_all %>%
group_by(date, nhs_region) %>%
summarise(tot = sum(n)) %>%
left_join(tab_date_region_age_all, by = c("date", "nhs_region")) %>%
mutate(prop_n = n/tot)
tab_date_region_age_all %>%
ggplot(aes(x = date, y = prop_n, color = age)) +
scale_color_manual(values = age.pal) +
geom_line() +
geom_point() +
geom_hline(data = nhs_region_pop, aes(yintercept = value, color = variable)) +
theme_bw() +
scale_weeks +
theme(legend.position = "bottom",
axis.text.x = element_text(angle = 90, hjust = 1)) +
guides(color = guide_legend(title = "Age", ncol = 3)) +
labs(x = NULL,
y = "Proportion of daily reports by age") +
facet_wrap(~ nhs_region, ncol = 4)We fit quasi-Poisson GLMs for 14-day windows to get growth rates over time.
## set moving time window (1/2/3 weeks)
w <- 14
# create empty df
r_all_sliding <- NULL
## make data for model
x_model_all_moving <- x %>%
filter(!is.na(nhs_region)) %>%
group_by(date, nhs_region) %>%
summarise(n = sum(count))
unique_dates <- unique(x_model_all_moving$date)
for (i in 1:(length(unique_dates) - w)) {
date_i <- unique_dates[i]
date_i_max <- date_i + w
model_data <- x_model_all_moving %>%
filter(date >= date_i & date < date_i_max) %>%
mutate(day = as.integer(date - date_i)) %>%
day_of_week()
mod <- glm(n ~ day * nhs_region + day_of_week,
data = model_data,
family = 'quasipoisson')
# get growth rate
r <- get_r(mod)
r$w_min <- date_i
r$w_max <- date_i_max
# combine all estimates
r_all_sliding <- bind_rows(r_all_sliding, r)
}
#serial interval distribution
SI_param = epitrix::gamma_mucv2shapescale(4.7, 2.9/4.7)
SI_distribution <- distcrete::distcrete("gamma", interval = 1,
shape = SI_param$shape,
scale = SI_param$scale,
w = 0.5)
#convert growth rates r to R0
r_all_sliding <- r_all_sliding %>%
mutate(R = epitrix::r2R0(r, SI_distribution),
R_lower_95 = epitrix::r2R0(lower_95, SI_distribution),
R_upper_95 = epitrix::r2R0(upper_95, SI_distribution))We examine the evolution of the growth rate by region over time.
# plot
plot_growth <-
r_all_sliding %>%
ggplot(aes(x = w_max, y = r)) +
geom_ribbon(aes(ymin = lower_95, ymax = upper_95, fill = nhs_region), alpha = 0.1) +
geom_line(aes(colour = nhs_region)) +
geom_point(aes(colour = nhs_region)) +
geom_hline(yintercept = 0, linetype = "dashed") +
theme_bw() +
scale_weeks +
theme(legend.position = "bottom",
plot.margin = margin(0.5,1,0.5,0.5, "cm")) +
guides(colour = guide_legend(title = "", override.aes = list(fill = NA)), fill = FALSE) +
labs(x = "",
y = "Estimated daily growth rate (r)") +
scale_colour_manual(values = pal)From the growth rate, we derive R and examine its value through time.
# plot
plot_R <-
r_all_sliding %>%
ggplot(aes(x = w_max, y = R)) +
geom_ribbon(aes(ymin = R_lower_95, ymax = R_upper_95, fill = nhs_region), alpha = 0.1) +
geom_line(aes(colour = nhs_region)) +
geom_point(aes(colour = nhs_region)) +
geom_hline(yintercept = 1, linetype = "dashed") +
theme_bw() +
scale_weeks +
theme(legend.position = "bottom",
plot.margin = margin(0.5,1,0.5,0.5, "cm")) +
guides(color = guide_legend(title = "", override.aes = list(fill = NA)), fill = FALSE) +
labs(x = "",
y = "Estimated effective reproduction\nnumber (Re)") +
scale_colour_manual(values = pal)
R <- r_all_sliding %>%
mutate(lower_95 = R_lower_95,
upper_95 = R_upper_95,
value = R,
measure = "R",
reference = 1)
r_R <- r_all_sliding %>%
mutate(measure = "r",
value = r,
reference = 0) %>%
bind_rows(R)
r_R %>%
ggplot(aes(x = w_max, y = value)) +
geom_ribbon(aes(ymin = lower_95, ymax = upper_95, fill = nhs_region), alpha = 0.1) +
geom_line(aes(colour = nhs_region)) +
geom_point(aes(colour = nhs_region)) +
geom_hline(aes(yintercept = reference), linetype = "dashed") +
theme_bw() +
scale_weeks +
rotate_x +
theme(legend.position = "bottom",
plot.margin = margin(0.5,1,0,0, "cm"),
strip.background = element_blank(),
# strip.text.x = element_blank(),
strip.placement = "outside"
) +
guides(color = guide_legend(title = "",
override.aes = list(fill = NA)),
fill = FALSE) +
labs(x = "", y = "") +
scale_colour_manual(values = pal) +
facet_grid(rows = vars(measure),
scales = "free_y",
switch = "y",
labeller = as_labeller(c(r = "Daily growth rate (r)",
R = "Effective reproduction\nnumber (Re)")))We repeat the above analysis, where we fit quasi-Poisson GLMs for 14-day windows to get growth rates over time, but apply this to each age group separately (0-18, 19-69, 70-120 years old).
We first run the analysis for 0-18 years old.
## set moving time window (2 weeks)
w <- 14
# create empty df
r_all_sliding_0_18 <- NULL
## make data for model
x_model_all_moving_0_18 <- x %>%
filter(!is.na(nhs_region),
age == "0-18") %>%
group_by(date, nhs_region) %>%
summarise(n = sum(count))
unique_dates <- unique(x_model_all_moving_0_18$date)
for (i in 1:(length(unique_dates) - w)) {
date_i <- unique_dates[i]
date_i_max <- date_i + w
model_data <- x_model_all_moving_0_18 %>%
filter(date >= date_i & date < date_i_max) %>%
mutate(day = as.integer(date - date_i)) %>%
day_of_week()
mod <- glm(n ~ day * nhs_region + day_of_week,
data = model_data,
family = 'quasipoisson')
# get growth rate
r <- get_r(mod)
r$w_min <- date_i
r$w_max <- date_i_max
# combine all estimates
r_all_sliding_0_18 <- bind_rows(r_all_sliding_0_18, r)
}
#serial interval distribution
SI_param = epitrix::gamma_mucv2shapescale(4.7, 2.9/4.7)
SI_distribution <- distcrete::distcrete("gamma", interval = 1,
shape = SI_param$shape,
scale = SI_param$scale, w = 0.5)
#convert growth rates r to R0
r_all_sliding_0_18 <- r_all_sliding_0_18 %>%
mutate(R = epitrix::r2R0(r, SI_distribution),
R_lower_95 = epitrix::r2R0(lower_95, SI_distribution),
R_upper_95 = epitrix::r2R0(upper_95, SI_distribution))# plot
plot_growth <-
r_all_sliding_0_18 %>%
ggplot(aes(x = w_max, y = r)) +
geom_ribbon(aes(ymin = lower_95, ymax = upper_95, fill = nhs_region), alpha = 0.1) +
geom_line(aes(colour = nhs_region)) +
geom_point(aes(colour = nhs_region)) +
geom_hline(yintercept = 0, linetype = "dashed") +
theme_bw() +
scale_weeks +
theme(legend.position = "bottom",
plot.margin = margin(0.5,1,0.5,0.5, "cm")) +
guides(colour = guide_legend(title = "",override.aes = list(fill = NA)), fill = FALSE) +
labs(x = "",
y = "Estimated daily growth rate (r)"
) +
scale_colour_manual(values = pal)# plot
plot_R <-
r_all_sliding_0_18 %>%
ggplot(aes(x = w_max, y = R)) +
geom_ribbon(aes(ymin = R_lower_95, ymax = R_upper_95, fill = nhs_region), alpha = 0.1) +
geom_line(aes(colour = nhs_region)) +
geom_point(aes(colour = nhs_region)) +
geom_hline(yintercept = 1, linetype = "dashed") +
theme_bw() +
scale_weeks +
theme(legend.position = "bottom",
plot.margin = margin(0.5,1,0.5,0.5, "cm")) +
guides(color = guide_legend(title = "", override.aes = list(fill = NA)), fill = FALSE) +
labs(x = "",
y = "Estimated effective reproduction\nnumber (Re)"
) +
scale_colour_manual(values = pal)
R <- r_all_sliding_0_18 %>%
mutate(lower_95 = R_lower_95,
upper_95 = R_upper_95,
value = R,
measure = "R",
reference = 1)
r_R <- r_all_sliding_0_18 %>%
mutate(measure = "r",
value = r,
reference = 0) %>%
bind_rows(R)
fig2_3_0_18 <- r_R %>%
ggplot(aes(x = w_max, y = value)) +
geom_ribbon(aes(ymin = lower_95, ymax = upper_95, fill = nhs_region), alpha = 0.1) +
geom_line(aes(colour = nhs_region)) +
geom_point(aes(colour = nhs_region)) +
geom_hline(aes(yintercept = reference), linetype = "dashed") +
theme_bw() +
scale_weeks +
theme(legend.position = "bottom",
plot.margin = margin(0.5,1,0,0, "cm"),
strip.background = element_blank(),
strip.placement = "outside"
) +
guides(color = guide_legend(title = "", override.aes = list(fill = NA)), fill = FALSE) +
labs(x = "", y = "") +
scale_colour_manual(values = pal) +
facet_grid(rows = vars(measure),
scales = "free_y",
switch = "y",
labeller = as_labeller(c(r = "Daily growth rate (r)",
R = "Effective reproduction\nnumber (Re)")))Then, we run the analysis for 19-69 years old.
## set moving time window (2 weeks)
w <- 14
# create empty df
r_all_sliding_19_69 <- NULL
## make data for model
x_model_all_moving_19_69 <- x %>%
filter(!is.na(nhs_region),
age == "19-69") %>%
group_by(date, nhs_region) %>%
summarise(n = sum(count))
unique_dates <- unique(x_model_all_moving_19_69$date)
for (i in 1:(length(unique_dates) - w)) {
date_i <- unique_dates[i]
date_i_max <- date_i + w
model_data <- x_model_all_moving_19_69 %>%
filter(date >= date_i & date < date_i_max) %>%
mutate(day = as.integer(date - date_i)) %>%
day_of_week()
mod <- glm(n ~ day * nhs_region + day_of_week,
data = model_data,
family = 'quasipoisson')
# get growth rate
r <- get_r(mod)
r$w_min <- date_i
r$w_max <- date_i_max
# combine all estimates
r_all_sliding_19_69 <- bind_rows(r_all_sliding_19_69, r)
}
#serial interval distribution
SI_param = epitrix::gamma_mucv2shapescale(4.7, 2.9/4.7)
SI_distribution <- distcrete::distcrete("gamma", interval = 1,
shape = SI_param$shape,
scale = SI_param$scale, w = 0.5)
#convert growth rates r to R0
r_all_sliding_19_69 <- r_all_sliding_19_69 %>%
mutate(R = epitrix::r2R0(r, SI_distribution),
R_lower_95 = epitrix::r2R0(lower_95, SI_distribution),
R_upper_95 = epitrix::r2R0(upper_95, SI_distribution))# plot
plot_growth <-
r_all_sliding_19_69 %>%
ggplot(aes(x = w_max, y = r)) +
geom_ribbon(aes(ymin = lower_95, ymax = upper_95, fill = nhs_region), alpha = 0.1) +
geom_line(aes(colour = nhs_region)) +
geom_point(aes(colour = nhs_region)) +
geom_hline(yintercept = 0, linetype = "dashed") +
theme_bw() +
scale_weeks +
theme(legend.position = "bottom",
plot.margin = margin(0.5,1,0.5,0.5, "cm")) +
guides(colour = guide_legend(title = "", override.aes = list(fill = NA)), fill = FALSE) +
labs(x = "",
y = "Estimated daily growth rate (r)") +
scale_colour_manual(values = pal)# plot
plot_R <-
r_all_sliding_19_69 %>%
ggplot(aes(x = w_max, y = R)) +
geom_ribbon(aes(ymin = R_lower_95, ymax = R_upper_95, fill = nhs_region), alpha = 0.1) +
geom_line(aes(colour = nhs_region)) +
geom_point(aes(colour = nhs_region)) +
geom_hline(yintercept = 1, linetype = "dashed") +
theme_bw() +
scale_weeks +
theme(legend.position = "bottom",
plot.margin = margin(0.5,1,0.5,0.5, "cm")) +
guides(color = guide_legend(title = "", override.aes = list(fill = NA)), fill = FALSE) +
labs(x = "",
y = "Estimated effective reproduction\nnumber (Re)"
) +
scale_colour_manual(values = pal)
R <- r_all_sliding_19_69 %>%
mutate(lower_95 = R_lower_95,
upper_95 = R_upper_95,
value = R,
measure = "R",
reference = 1)
r_R <- r_all_sliding_19_69 %>%
mutate(measure = "r",
value = r,
reference = 0) %>%
bind_rows(R)
fig2_3_19_69 <- r_R %>%
ggplot(aes(x = w_max, y = value)) +
geom_ribbon(aes(ymin = lower_95, ymax = upper_95, fill = nhs_region), alpha = 0.1) +
geom_line(aes(colour = nhs_region)) +
geom_point(aes(colour = nhs_region)) +
geom_hline(aes(yintercept = reference), linetype = "dashed") +
theme_bw() +
scale_weeks +
theme(legend.position = "bottom",
plot.margin = margin(0.5,1,0,0, "cm"),
strip.background = element_blank(),
strip.placement = "outside"
) +
guides(color = guide_legend(title = "", override.aes = list(fill = NA)), fill = FALSE) +
labs(x = "", y = "") +
scale_colour_manual(values = pal) +
facet_grid(rows = vars(measure),
scales = "free_y",
switch = "y",
labeller = as_labeller(c(r = "Daily growth rate (r)",
R = "Effective reproduction\nnumber (Re)")))Finally, we run the analysis for 70-120 years old.
## set moving time window (2 weeks)
w <- 14
# create empty df
r_all_sliding_70_120 <- NULL
## make data for model
x_model_all_moving_70_120 <- x %>%
filter(!is.na(nhs_region),
age == "70-120") %>%
group_by(date, nhs_region) %>%
summarise(n = sum(count))
unique_dates <- unique(x_model_all_moving_70_120$date)
for (i in 1:(length(unique_dates) - w)) {
date_i <- unique_dates[i]
date_i_max <- date_i + w
model_data <- x_model_all_moving_70_120 %>%
filter(date >= date_i & date < date_i_max) %>%
mutate(day = as.integer(date - date_i)) %>%
day_of_week()
mod <- glm(n ~ day * nhs_region + day_of_week,
data = model_data,
family = 'quasipoisson')
# get growth rate
r <- get_r(mod)
r$w_min <- date_i
r$w_max <- date_i_max
# combine all estimates
r_all_sliding_70_120 <- bind_rows(r_all_sliding_70_120, r)
}
#serial interval distribution
SI_param = epitrix::gamma_mucv2shapescale(4.7, 2.9/4.7)
SI_distribution <- distcrete::distcrete("gamma", interval = 1,
shape = SI_param$shape,
scale = SI_param$scale, w = 0.5)
#convert growth rates r to R0
r_all_sliding_70_120 <- r_all_sliding_70_120 %>%
mutate(R = epitrix::r2R0(r, SI_distribution),
R_lower_95 = epitrix::r2R0(lower_95, SI_distribution),
R_upper_95 = epitrix::r2R0(upper_95, SI_distribution))# plot
plot_growth <-
r_all_sliding_70_120 %>%
ggplot(aes(x = w_max, y = r)) +
geom_ribbon(aes(ymin = lower_95, ymax = upper_95, fill = nhs_region), alpha = 0.1) +
geom_line(aes(colour = nhs_region)) +
geom_point(aes(colour = nhs_region)) +
geom_hline(yintercept = 0, linetype = "dashed") +
theme_bw() +
scale_weeks +
theme(legend.position = "bottom",
plot.margin = margin(0.5,1,0.5,0.5, "cm")) +
guides(colour = guide_legend(title = "",override.aes = list(fill = NA)), fill = FALSE) +
labs(x = "",
y = "Estimated daily growth rate (r)"
) +
scale_colour_manual(values = pal)# plot
plot_R <-
r_all_sliding_70_120 %>%
ggplot(aes(x = w_max, y = R)) +
geom_ribbon(aes(ymin = R_lower_95, ymax = R_upper_95, fill = nhs_region), alpha = 0.1) +
geom_line(aes(colour = nhs_region)) +
geom_point(aes(colour = nhs_region)) +
geom_hline(yintercept = 1, linetype = "dashed") +
theme_bw() +
scale_weeks +
theme(legend.position = "bottom",
plot.margin = margin(0.5,1,0.5,0.5, "cm")) +
guides(color = guide_legend(title = "", override.aes = list(fill = NA)), fill = FALSE) +
labs(x = "",
y = "Estimated effective reproduction\nnumber (Re)") +
scale_colour_manual(values = pal)
R <- r_all_sliding_70_120 %>%
mutate(lower_95 = R_lower_95,
upper_95 = R_upper_95,
value = R,
measure = "R",
reference = 1)
r_R <- r_all_sliding_70_120 %>%
mutate(measure = "r",
value = r,
reference = 0) %>%
bind_rows(R)
fig2_3_70_120 <- r_R %>%
ggplot(aes(x = w_max, y = value)) +
geom_ribbon(aes(ymin = lower_95, ymax = upper_95, fill = nhs_region), alpha = 0.1) +
geom_line(aes(colour = nhs_region)) +
geom_point(aes(colour = nhs_region)) +
geom_hline(aes(yintercept = reference), linetype = "dashed") +
theme_bw() +
scale_weeks +
theme(legend.position = "bottom",
plot.margin = margin(0.5,1,0,0, "cm"),
strip.background = element_blank(),
strip.placement = "outside"
) +
guides(color = guide_legend(title = "", override.aes = list(fill = NA)), fill = FALSE) +
labs(x = "", y = "") +
scale_colour_manual(values = pal) +
facet_grid(rows = vars(measure),
scales = "free_y",
switch = "y",
labeller = as_labeller(c(r = "Daily growth rate (r)",
R = "Effective reproduction\nnumber (Re)"))) We combine the estimated growth rates and effective reproduction numbers into a single figure.
ggpubr::ggarrange(fig2_3_0_18,
fig2_3_19_69,
fig2_3_70_120,
nrow = 3,
labels = "AUTO",
common.legend = TRUE,
legend = "bottom",
align = "hv") We want to explore the correlation between NHS Pathways reports and deaths, and assess the potential for reports to be used as an early warning system for disease resurgence.
Death data are publically available. We truncate the time series to avoid bias from reporting delay - we assume a conservative delay of three weeks.
We calculate Pearson’s correlation coefficient between deaths and NHS Pathways notifications using different lags. Confidence intervals are obtained using bootstrap. Note that results were also confirmed using Spearman’s rank correlation.
First we join the NHS Pathways and death data, and aggregate over all England:
## truncate death data for reporting delay
trunc_date <- max(dth$date_report) - delay_max
dth_trunc <- dth %>%
rename(date = date_report) %>%
filter(date <= trunc_date)
## join with notification data
all_data <- x %>%
filter(!is.na(nhs_region)) %>%
group_by(date, nhs_region) %>%
summarise(count = sum(count, na.rm = T)) %>%
ungroup %>%
inner_join(dth_trunc,
by = c("date","nhs_region"))
all_tot <- all_data %>%
group_by(date) %>%
summarise(count = sum(count, na.rm = TRUE),
deaths = sum(deaths, na.rm = TRUE)) We calculate correlation with lagged NHS Pathways reports from 0 to 30 days behind deaths:
## Calculate all correlations + bootstrap CIs
lag_cor <- data.frame()
for (i in 0:30) {
## lag reports
summary <- all_tot %>%
mutate(note_lag = lag(count, i)) %>%
## calculate rank correlation and bootstrap CI
getboot(.) %>%
mutate(lag = i)
lag_cor <- bind_rows(lag_cor, summary)
}
cor_vs_lag <- ggplot(lag_cor, aes(lag, r)) +
theme_bw() +
geom_ribbon(aes(ymin = r_low, ymax = r_hi), alpha = 0.2) +
geom_hline(yintercept = 0, lty = "longdash") +
geom_point() +
geom_line() +
labs(x = "Lag between NHS pathways and death data (days)",
y = "Pearson's correlation") +
large_txt
cor_vs_lagThis analysis suggests that the best lag is 23 days. We then compare and plot the number of deaths reported against the number of NHS Pathways reports lagged by 23 days.
all_tot <- all_tot %>%
rename(date_death = date) %>%
mutate(note_lag = lag(count, lag_cor$lag[l_opt]),
note_lag_c = (note_lag - mean(note_lag, na.rm = T)),
date_note = lag(date_death,16))
lag_mod <- glm(deaths ~ note_lag, data = all_tot, family = "quasipoisson")
summary(lag_mod)
##
## Call:
## glm(formula = deaths ~ note_lag, family = "quasipoisson", data = all_tot)
##
## Deviance Residuals:
## Min 1Q Median 3Q Max
## -10.7556 -3.0649 -0.3555 3.6837 6.1641
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 4.822e+00 5.545e-02 86.97 <2e-16 ***
## note_lag 1.266e-05 5.672e-07 22.32 <2e-16 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## (Dispersion parameter for quasipoisson family taken to be 14.64047)
##
## Null deviance: 7769.26 on 58 degrees of freedom
## Residual deviance: 862.83 on 57 degrees of freedom
## (23 observations deleted due to missingness)
## AIC: NA
##
## Number of Fisher Scoring iterations: 4
exp(coefficients(lag_mod))
## (Intercept) note_lag
## 124.233941 1.000013
exp(confint(lag_mod))
## 2.5 % 97.5 %
## (Intercept) 111.290553 138.314860
## note_lag 1.000012 1.000014
Rsq(lag_mod)
## [1] 0.8889433
mod_fit <- as.data.frame(predict(lag_mod, type = "link", se.fit = TRUE)[1:2])
all_tot_pred <-
all_tot %>%
filter(!is.na(note_lag)) %>%
mutate(pred = mod_fit$fit,
pred.se = mod_fit$se.fit,
low = exp(pred - 1.96*pred.se),
hi = exp(pred + 1.96*pred.se))
glm_fit <- all_tot_pred %>%
filter(!is.na(note_lag)) %>%
ggplot(aes(x = note_lag, y = deaths)) +
geom_point() +
geom_line(aes(y = exp(pred))) +
geom_ribbon(aes(ymin = low, ymax = hi), alpha = 0.3, col = "grey") +
theme_bw() +
labs(y = "Daily number of\ndeaths reported",
x = "Daily number of NHS Pathways reports") +
large_txt
glm_fitThis is a comparison of gamma versus lognormal distribution for the serial interval used to convert r to R in our analysis. Both distributions are parameterised with mean 4.7 and standard deviation 2.9.
SI_param <- epitrix::gamma_mucv2shapescale(4.7, 2.9/4.7)
SI_distribution <- distcrete::distcrete("gamma", interval = 1,
shape = SI_param$shape,
scale = SI_param$scale, w = 0.5)
SI_distribution2 <- distcrete::distcrete("lnorm", interval = 1,
meanlog = log(4.7),
sdlog = log(2.9), w = 0.5)
SI_dist1 <- data.frame(x = SI_distribution$r(1e5))
SI_dist1 <- count(SI_dist1, x) %>%
ggplot() +
geom_col(aes(x = x, y = n)) +
labs(x = "Serial interval (days)", y = "Frequency") +
scale_x_continuous(breaks = seq(0, 30, 5)) +
theme_bw()
SI_dist2 <- data.frame(x = SI_distribution2$r(1e5))
SI_dist2 <- count(SI_dist2, x) %>%
ggplot() +
geom_col(aes(x = x, y = n)) +
labs(x = "Serial interval (days)", y = "Frequency") +
scale_x_continuous(breaks = seq(0, 200, 20), limits = c(0, 200)) +
theme_bw()
ggpubr::ggarrange(SI_dist1,
SI_dist2,
nrow = 1,
labels = "AUTO") We reproduce the window analysis with either a 7 or 21 days window for sensitivity purposes.
First with the 7 days window:
## set moving time window (1/2/3 weeks)
w <- 7
# create empty df
r_all_sliding_7days <- NULL
## make data for model
x_model_all_moving <- x %>%
filter(!is.na(nhs_region)) %>%
group_by(date, nhs_region) %>%
summarise(n = sum(count))
unique_dates <- unique(x_model_all_moving$date)
for (i in 1:(length(unique_dates) - w)) {
date_i <- unique_dates[i]
date_i_max <- date_i + w
model_data <- x_model_all_moving %>%
filter(date >= date_i & date < date_i_max) %>%
mutate(day = as.integer(date - date_i)) %>%
day_of_week()
mod <- glm(n ~ day * nhs_region + day_of_week,
data = model_data,
family = 'quasipoisson')
# get growth rate
r <- get_r(mod)
r$w_min <- date_i
r$w_max <- date_i_max
# combine all estimates
r_all_sliding_7days <- bind_rows(r_all_sliding_7days, r)
}
#serial interval distribution
SI_param = epitrix::gamma_mucv2shapescale(4.7, 2.9/4.7)
SI_distribution <- distcrete::distcrete("gamma", interval = 1,
shape = SI_param$shape,
scale = SI_param$scale,
w = 0.5)
#convert growth rates r to R0
r_all_sliding_7days <- r_all_sliding_7days %>%
mutate(R = epitrix::r2R0(r, SI_distribution),
R_lower_95 = epitrix::r2R0(lower_95, SI_distribution),
R_upper_95 = epitrix::r2R0(upper_95, SI_distribution))# plot
plot_growth <-
r_all_sliding_7days %>%
ggplot(aes(x = w_max, y = r)) +
geom_ribbon(aes(ymin = lower_95, ymax = upper_95, fill = nhs_region), alpha = 0.1) +
geom_line(aes(colour = nhs_region)) +
geom_point(aes(colour = nhs_region)) +
geom_hline(yintercept = 0, linetype = "dashed") +
theme_bw() +
scale_weeks +
theme(legend.position = "bottom",
plot.margin = margin(0.5,1,0.5,0.5, "cm")) +
guides(colour = guide_legend(title = "",override.aes = list(fill = NA)), fill = FALSE) +
labs(x = "",
y = "Estimated daily growth rate (r)") +
scale_colour_manual(values = pal)plot_R <- r_all_sliding_7days %>%
ggplot(aes(x = w_max, y = R)) +
geom_ribbon(aes(ymin = R_lower_95, ymax = R_upper_95, fill = nhs_region), alpha = 0.1) +
geom_line(aes(colour = nhs_region)) +
geom_point(aes(colour = nhs_region)) +
geom_hline(yintercept = 1, linetype = "dashed") +
theme_bw() +
scale_weeks +
theme(legend.position = "bottom",
plot.margin = margin(0.5,1,0.5,0.5, "cm")) +
guides(color = guide_legend(title = "", override.aes = list(fill = NA)), fill = FALSE) +
labs(x = "",
y = "Estimated effective reproduction\nnumber (Re)") +
scale_colour_manual(values = pal)
R <- r_all_sliding_7days %>%
mutate(lower_95 = R_lower_95,
upper_95 = R_upper_95,
value = R,
measure = "R",
reference = 1)
r_R <- r_all_sliding_7days %>%
mutate(measure = "r",
value = r,
reference = 0) %>%
bind_rows(R)
r_R_7 <- r_R %>%
ggplot(aes(x = w_max, y = value)) +
geom_ribbon(aes(ymin = lower_95, ymax = upper_95, fill = nhs_region), alpha = 0.1) +
geom_line(aes(colour = nhs_region)) +
geom_point(aes(colour = nhs_region)) +
geom_hline(aes(yintercept = reference), linetype = "dashed") +
theme_bw() +
scale_weeks +
theme(legend.position = "bottom",
plot.margin = margin(0.5,1,0,0, "cm"),
strip.background = element_blank(),
strip.placement = "outside"
) +
guides(color = guide_legend(title = "", override.aes = list(fill = NA)), fill = FALSE) +
labs(x = "", y = "") +
scale_colour_manual(values = pal) +
facet_grid(rows = vars(measure),
scales = "free_y",
switch = "y",
labeller = as_labeller(c(r = "Daily growth rate (r)",
R = "Effective reproduction\nnumber (Re)")))Then with the 21 days window:
## set moving time window (1/2/3 weeks)
w <- 21
# create empty df
r_all_sliding_21days <- NULL
## make data for model
x_model_all_moving <- x %>%
filter(!is.na(nhs_region)) %>%
group_by(date, nhs_region) %>%
summarise(n = sum(count))
unique_dates <- unique(x_model_all_moving$date)
for (i in 1:(length(unique_dates) - w)) {
date_i <- unique_dates[i]
date_i_max <- date_i + w
model_data <- x_model_all_moving %>%
filter(date >= date_i & date < date_i_max) %>%
mutate(day = as.integer(date - date_i)) %>%
day_of_week()
mod <- glm(n ~ day * nhs_region + day_of_week,
data = model_data,
family = 'quasipoisson')
# get growth rate
r <- get_r(mod)
r$w_min <- date_i
r$w_max <- date_i_max
# combine all estimates
r_all_sliding_21days <- bind_rows(r_all_sliding_21days, r)
}
#serial interval distribution
SI_param = epitrix::gamma_mucv2shapescale(4.7, 2.9/4.7)
SI_distribution <- distcrete::distcrete("gamma", interval = 1,
shape = SI_param$shape,
scale = SI_param$scale,
w = 0.5)
#convert growth rates r to R0
r_all_sliding_21days <- r_all_sliding_21days %>%
mutate(R = epitrix::r2R0(r, SI_distribution),
R_lower_95 = epitrix::r2R0(lower_95, SI_distribution),
R_upper_95 = epitrix::r2R0(upper_95, SI_distribution))# plot
plot_growth <-
r_all_sliding_21days %>%
ggplot(aes(x = w_max, y = r)) +
geom_ribbon(aes(ymin = lower_95, ymax = upper_95, fill = nhs_region), alpha = 0.1) +
geom_line(aes(colour = nhs_region)) +
geom_point(aes(colour = nhs_region)) +
geom_hline(yintercept = 0, linetype = "dashed") +
theme_bw() +
scale_weeks +
theme(legend.position = "bottom",
plot.margin = margin(0.5,1,0.5,0.5, "cm")) +
guides(colour = guide_legend(title = "",override.aes = list(fill = NA)), fill = FALSE) +
labs(x = "",
y = "Estimated daily growth rate (r)") +
scale_colour_manual(values = pal)# plot
plot_R <-
r_all_sliding_21days %>%
ggplot(aes(x = w_max, y = R)) +
geom_ribbon(aes(ymin = R_lower_95, ymax = R_upper_95, fill = nhs_region), alpha = 0.1) +
geom_line(aes(colour = nhs_region)) +
geom_point(aes(colour = nhs_region)) +
geom_hline(yintercept = 1, linetype = "dashed") +
theme_bw() +
scale_weeks +
theme(legend.position = "bottom",
plot.margin = margin(0.5,1,0.5,0.5, "cm")) +
guides(color = guide_legend(title = "", override.aes = list(fill = NA)), fill = FALSE) +
labs(x = "",
y = "Estimated effective reproduction\nnumber (Re)") +
scale_colour_manual(values = pal)
R <- r_all_sliding_21days %>%
mutate(lower_95 = R_lower_95,
upper_95 = R_upper_95,
value = R,
measure = "R",
reference = 1)
r_R <- r_all_sliding_21days %>%
mutate(measure = "r",
value = r,
reference = 0) %>%
bind_rows(R)
r_R_21 <- r_R %>%
ggplot(aes(x = w_max, y = value)) +
geom_ribbon(aes(ymin = lower_95, ymax = upper_95, fill = nhs_region), alpha = 0.1) +
geom_line(aes(colour = nhs_region)) +
geom_point(aes(colour = nhs_region)) +
geom_hline(aes(yintercept = reference), linetype = "dashed") +
theme_bw() +
scale_weeks +
theme(legend.position = "bottom",
plot.margin = margin(0.5,1,0,0, "cm"),
strip.background = element_blank(),
strip.placement = "outside"
) +
guides(color = guide_legend(title = "", override.aes = list(fill = NA)), fill = FALSE) +
labs(x = "", y = "") +
scale_colour_manual(values = pal) +
facet_grid(rows = vars(measure),
scales = "free_y",
switch = "y",
labeller = as_labeller(c(r = "Daily growth rate (r)",
R = "Effective reproduction\nnumber (Re)")))And we combine both outputs into a single plot:
ggpubr::ggarrange(r_R_7,
r_R_21,
nrow = 2,
labels = "AUTO",
common.legend = TRUE,
legend = "bottom")
lag_cor_reg <- data.frame()
for (i in 0:30) {
summary <-
all_data %>%
group_by(nhs_region) %>%
mutate(note_lag = lag(count, i)) %>%
## calculate rank correlation and bootstrap CI for each region
group_modify(~getboot(.x)) %>%
mutate(lag = i)
lag_cor_reg <- bind_rows(lag_cor_reg, summary)
}
cor_vs_lag_reg <-
lag_cor_reg %>%
ggplot(aes(lag, r, col = nhs_region)) +
geom_hline(yintercept = 0, lty = "longdash") +
geom_ribbon(aes(ymin = r_low, ymax = r_hi, col = NULL, fill = nhs_region), alpha = 0.2) +
geom_point() +
geom_line() +
facet_wrap(~nhs_region) +
scale_color_manual(values = pal) +
scale_fill_manual(values = pal, guide = F) +
theme_bw() +
labs(x = "Lag between NHS pathways and death data (days)", y = "Pearson's correlation", col = "NHS region") +
theme(legend.position = "bottom") +
guides(color = guide_legend(override.aes = list(fill = NA)))
cor_vs_lag_regWe save the tables created during our analysis:
if (!dir.exists("excel_tables")) {
dir.create("excel_tables")
}
## list all tables, and loop over export
tables_to_export <- c("r_all_sliding", "lag_cor")
for (e in tables_to_export) {
rio::export(get(e),
file.path("excel_tables",
paste0(e, ".xlsx")))
}
## also export result from regression on lagged data
rio::export(lag_mod, file.path("excel_tables", "lag_mod.rds"))The following information documents the system on which the document was compiled.
This provides information on the operating system.
This provides information on the version of R used:
This provides information on the packages used:
sessionInfo()
## R version 4.0.2 (2020-06-22)
## Platform: x86_64-apple-darwin17.0 (64-bit)
## Running under: macOS Catalina 10.15.5
##
## Matrix products: default
## BLAS: /Library/Frameworks/R.framework/Versions/4.0/Resources/lib/libRblas.dylib
## LAPACK: /Library/Frameworks/R.framework/Versions/4.0/Resources/lib/libRlapack.dylib
##
## locale:
## [1] en_US.UTF-8/en_US.UTF-8/en_US.UTF-8/C/en_US.UTF-8/en_US.UTF-8
##
## attached base packages:
## [1] stats graphics grDevices utils datasets methods base
##
## other attached packages:
## [1] ggnewscale_0.4.1 ggpubr_0.4.0 lubridate_1.7.9
## [4] chngpt_2020.5-21 cyphr_1.1.0 DT_0.14
## [7] kableExtra_1.1.0 janitor_2.0.1 remotes_2.1.1
## [10] projections_0.5.1 earlyR_0.0.1 epitrix_0.2.2
## [13] distcrete_1.0.3 incidence_1.7.1 rio_0.5.16
## [16] reshape2_1.4.4 rvest_0.3.5 xml2_1.3.2
## [19] linelist_0.0.40.9000 forcats_0.5.0 stringr_1.4.0
## [22] dplyr_1.0.0 purrr_0.3.4 readr_1.3.1
## [25] tidyr_1.1.0 tibble_3.0.1 ggplot2_3.3.2
## [28] tidyverse_1.3.0 here_0.1 reportfactory_0.0.5
##
## loaded via a namespace (and not attached):
## [1] colorspace_1.4-1 selectr_0.4-2 ggsignif_0.6.0 ellipsis_0.3.1
## [5] rprojroot_1.3-2 snakecase_0.11.0 fs_1.4.1 rstudioapi_0.11
## [9] farver_2.0.3 fansi_0.4.1 splines_4.0.2 knitr_1.29
## [13] jsonlite_1.7.0 broom_0.5.6 dbplyr_1.4.4 compiler_4.0.2
## [17] httr_1.4.1 backports_1.1.8 assertthat_0.2.1 Matrix_1.2-18
## [21] cli_2.0.2 htmltools_0.5.0 prettyunits_1.1.1 tools_4.0.2
## [25] gtable_0.3.0 glue_1.4.1 Rcpp_1.0.4.6 carData_3.0-4
## [29] cellranger_1.1.0 vctrs_0.3.1 nlme_3.1-148 matchmaker_0.1.1
## [33] crosstalk_1.1.0.1 xfun_0.15 ps_1.3.3 openxlsx_4.1.5
## [37] lifecycle_0.2.0 rstatix_0.6.0 MASS_7.3-51.6 scales_1.1.1
## [41] hms_0.5.3 sodium_1.1 yaml_2.2.1 curl_4.3
## [45] gridExtra_2.3 stringi_1.4.6 kyotil_2019.11-22 boot_1.3-25
## [49] pkgbuild_1.0.8 zip_2.0.4 rlang_0.4.6 pkgconfig_2.0.3
## [53] evaluate_0.14 lattice_0.20-41 labeling_0.3 htmlwidgets_1.5.1
## [57] cowplot_1.0.0 processx_3.4.2 tidyselect_1.1.0 plyr_1.8.6
## [61] magrittr_1.5 R6_2.4.1 generics_0.0.2 DBI_1.1.0
## [65] pillar_1.4.4 haven_2.3.1 foreign_0.8-80 withr_2.2.0
## [69] mgcv_1.8-31 survival_3.1-12 abind_1.4-5 modelr_0.1.8
## [73] crayon_1.3.4 car_3.0-8 utf8_1.1.4 rmarkdown_2.3
## [77] viridis_0.5.1 grid_4.0.2 readxl_1.3.1 data.table_1.12.8
## [81] blob_1.2.1 callr_3.4.3 reprex_0.3.0 digest_0.6.25
## [85] webshot_0.5.2 munsell_0.5.0 viridisLite_0.3.0